Agricultural Valuation & Machine Learning Model SME at Praesignis

Role Purpose

• The Agricultural Valuation & Machine Learning Model SME will provide specialist advisory, research, and knowledge management services to support the development, validation, and interpretation of machine learning-driven agricultural valuation models.
• The role bridges agricultural valuation expertise, business requirements, data science, and credit decisioning to ensure valuation approaches are practical, credible, and aligned to business objectives.

Key Responsibilities

• Provide expert guidance on agricultural valuation methodologies and asset pricing principles.
• Advise on the development and refinement of machine learning valuation models.
• Support identification of valuation drivers, assumptions, and business rules.
• Review and interpret model outputs and provide recommendations.
• Conduct research on valuation practices, market trends, and benchmark data.
• Facilitate knowledge transfer sessions and stakeholder workshops.
• Translate technical modelling concepts into business language.
• Engage with Product, Credit Risk, Agriculture Banking, Data Science, Technology, and Project teams.
• Produce executive presentations, research papers, and recommendation reports
• Support governance, model review, and decision-making forums.

Required Skills and Experience

• Demonstrated experience in agricultural valuation, agribusiness economics, agricultural finance, or related disciplines.
• Strong understanding of valuation methodologies and collateral valuation principles.
• Experience supporting analytical, statistical, or machine learning models.
• Strong research, analytical, and documentation skills.
• Ability to communicate effectively with business, technical, and executive stakeholders.
• Experience within banking, agricultural finance, credit risk, or lending environments is advantageous.

Key Deliverables

• Agricultural valuation methodology recommendations.
• Model review, validation, and advisory feedback.
• Data and valuation research reports.
• Stakeholder education and knowledge transfer material.
• Executive briefing papers and presentation packs.
• Valuation framework documentation.
• Inputs into POC recommendations and implementation planning.

Success Measures

• Improved quality and credibility of valuation outputs.
• Increased stakeholder confidence in valuation methodologies.
• Effective translation of model outputs into business insights
• High-quality advisory and knowledge management support.
• Timely delivery of agreed research and documentation